Robot Adaptation to Unstructured Terrains by Joint Representation and Apprenticeship Learning

Conference · Fri Jul 12 04:00:00 EDT 2019

OSTI ID:1560517

Siva, Sriram ^[1]; Wigness, Maggie ^[2]; Rogers, John ^[2]; Zhang, Hao ^[3]

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1. Colorado School of Mines; Colorado School of Mines
2. Army Research Laboratory
3. Colorado School of Mines

When a mobile robot is deployed in a field environment, e.g., during a disaster response application, the capability of adapting its navigational behaviors to unstructured terrains is essential for effective and safe robot navigation. In this paper, we introduce a novel joint terrain representation and apprenticeship learning approach to implement robot adaptation to unstructured terrains. Different from conventional learning-based adaptation techniques, our approach provides a unified problem formulation that integrates representation and apprenticeship learning under a unified regularized optimization framework, instead of treating them as separate and independent procedures. Our approach also has the capability to automatically identify discriminative feature modalities, which can improve the robustness of robot adaptation. In addition, we implement a new optimization algorithm to solve the formulated problem, which provides a theoretical guarantee to converge to the global optimal solution. In the experiments, we extensively evaluate the proposed approach in real-world scenarios, in which a mobile robot navigates on familiar and unfamiliar unstructured terrains. Experimental results have shown that the proposed approach is able to transfer human expertise to robots with small errors, achieve superior performance compared with previous and baseline methods, and provide intuitive insights on the importance of terrain feature modalities.

• Research Organization: Colorado School of Mines, Golden, CO; Michigan State University, East Lansing, MI
• Sponsoring Organization: DOE-Mines-31650-02
• DOE Contract Number: FE0031650
• OSTI ID: 1560517
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
Robot Adaptation
Robotics